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train_baseline.py
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train_baseline.py
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"""
License: Apache-2.0
Author: Suofei Zhang | Hang Yu
E-mail: zhangsuofei at njupt.edu.cn | hangyu5 at illinois.edu
"""
import tensorflow as tf
import tensorflow.contrib.slim as slim
from config import cfg, get_coord_add, get_dataset_size_train, get_num_classes, get_create_inputs
import time
import numpy as np
import sys
import os
import model as net
import logging
import daiquiri
daiquiri.setup(level=logging.DEBUG)
logger = daiquiri.getLogger(__name__)
def main(args):
"""Get dataset hyperparameters."""
assert len(args) == 2 and isinstance(args[1], str)
dataset_name = args[1]
logger.info('Using dataset: {}'.format(dataset_name))
"""Set reproduciable random seed"""
tf.set_random_seed(1234)
coord_add = get_coord_add(dataset_name)
dataset_size = get_dataset_size_train(dataset_name)
num_classes = get_num_classes(dataset_name)
create_inputs = get_create_inputs(dataset_name, is_train=True, epochs=cfg.epoch)
with tf.Graph().as_default(), tf.device('/cpu:0'):
"""Get global_step."""
global_step = tf.get_variable(
'global_step', [], initializer=tf.constant_initializer(0), trainable=False)
"""Get batches per epoch."""
num_batches_per_epoch = int(dataset_size / cfg.batch_size)
"""Use exponential decay leanring rate?"""
lrn_rate = tf.maximum(tf.train.exponential_decay(
1e-3, global_step, num_batches_per_epoch, 0.8), 1e-5)
tf.summary.scalar('learning_rate', lrn_rate)
opt = tf.train.AdamOptimizer() # lrn_rate
"""Get batch from data queue."""
batch_x, batch_labels = create_inputs()
# batch_y = tf.one_hot(batch_labels, depth=10, axis=1, dtype=tf.float32)
"""Define the dataflow graph."""
with tf.device('/gpu:0'):
with slim.arg_scope([slim.variable], device='/cpu:0'):
batch_x_squash = tf.divide(batch_x, 255.)
batch_x = slim.batch_norm(batch_x, center=False, is_training=True, trainable=True)
output, _ = net.build_arch_baseline(batch_x, is_train=True,
num_classes=num_classes)
loss, recon_loss, _ = net.cross_ent_loss(output, batch_x_squash, batch_labels)
acc = net.test_accuracy(output, batch_labels)
tf.summary.scalar('train_acc', acc)
tf.summary.scalar('recon_loss', recon_loss)
tf.summary.scalar('all_loss', loss)
"""Compute gradient."""
grad = opt.compute_gradients(loss)
# See: https://stackoverflow.com/questions/40701712/how-to-check-nan-in-gradients-in-tensorflow-when-updating
grad_check = [tf.check_numerics(g, message='Gradient NaN Found!')
for g, _ in grad if g is not None] + [tf.check_numerics(loss, message='Loss NaN Found')]
"""Apply graident."""
with tf.control_dependencies(grad_check):
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = opt.apply_gradients(grad, global_step=global_step)
"""Set Session settings."""
sess = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True, log_device_placement=False))
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())
"""Set Saver."""
var_to_save = [v for v in tf.global_variables(
) if 'Adam' not in v.name] # Don't save redundant Adam beta/gamma
saver = tf.train.Saver(var_list=var_to_save, max_to_keep=cfg.epoch)
"""Display parameters"""
total_p = np.sum([np.prod(v.get_shape().as_list()) for v in var_to_save]).astype(np.int32)
train_p = np.sum([np.prod(v.get_shape().as_list())
for v in tf.trainable_variables()]).astype(np.int32)
logger.info('Total Parameters: {}'.format(total_p))
logger.info('Trainable Parameters: {}'.format(train_p))
# read snapshot
# latest = os.path.join(cfg.logdir, 'model.ckpt-4680')
# saver.restore(sess, latest)
"""Set summary op."""
summary_op = tf.summary.merge_all()
"""Start coord & queue."""
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
"""Set summary writer"""
if not os.path.exists(cfg.logdir + '/cnn_baseline/{}/train_log/'.format(dataset_name)):
os.makedirs(cfg.logdir + '/cnn_baseline/{}/train_log/'.format(dataset_name))
summary_writer = tf.summary.FileWriter(
cfg.logdir + '/cnn_baseline/{}/train_log/'.format(dataset_name), graph=sess.graph)
"""Main loop."""
for step in range(cfg.epoch * num_batches_per_epoch + 1):
tic = time.time()
""""TF queue would pop batch until no file"""
try:
_, loss_value, summary_str = sess.run(
[train_op, loss, summary_op])
logger.info('%d iteration finishs in ' % step + '%f second' %
(time.time() - tic) + ' loss=%f' % loss_value)
except KeyboardInterrupt:
sess.close()
sys.exit()
except tf.errors.InvalidArgumentError:
logger.warning('%d iteration contains NaN gradients. Discard.' % step)
continue
else:
"""Write to summary."""
if step % 5 == 0:
summary_writer.add_summary(summary_str, step)
if ((step % num_batches_per_epoch*cfg.epoch/10)) == 0:
"""Save model periodically"""
ckpt_path = os.path.join(
cfg.logdir + '/cnn_baseline/{}/'.format(dataset_name), 'model.ckpt'.format(step))
saver.save(sess, ckpt_path, global_step=step)
"""Join threads"""
coord.join(threads)
if __name__ == "__main__":
tf.app.run()